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Field
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is supported by NParks and aims to evaluate cooling effects of urban greenery and develop a modelling platform to assess and optimize the cooling benefits of various types and configurations of urban
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-time tenure-track faculty position in Applied Mathematics and Computational Modeling. We seek outstanding candidates whose research is centered on the mathematical modeling, analysis, numerical solution
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contribute to undergraduate and graduate teaching in areas such as Numerical Methods, Ordinary and Partial Differential Equations, Dynamical Systems, Optimization, Scientific Computing, Complex Systems, and
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primarily in traditional numerical analysis or computational modeling. The successful candidate will contribute to undergraduate and graduate teaching in areas such as Mathematics for Artificial Intelligence
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for computational mathematics. This includes but is not limited to: mathematical foundations of AI and machine learning, inverse problems, optimization, numerical analysis. Qualifications A Ph.D. in mathematics is
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/Optimization research group within the Department of Mathematics. The successful candidate is expected to establish a research and teaching focus in optimization. The Department is looking for a candidate with
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focus on the area of computational mathematics broadly construed. This includes optimization, mathematical foundations of machine learning and AI, AI for mathematics, and numerical analysis. Demonstrated
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biomedical engineering. Design and optimize three-dimensional printed structures using computer-aided design, numerical modelling, data-driven optimization, and artificial intelligence-assisted methods
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and predictions. Use cutting-edge methodologies such as A.I. and machine-learning to accelerate the numerical simulation processes. Develop optimal control strategies for energy systems Publish high
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, CUDA), numerical linear algebra, sparse matrix computation, and low-latency system design. · Machine Learning and AI Theory/Algorithm Design: optimization theory and algorithm design for deep learning